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Threshold-Based Noise Detection and Reduction for Automatic Speech Recognition System in Human-Robot Interactions.

Sheng-Chieh Lee1, Jhing-Fa Wang2, Miao-Hia Chen3

  • 1Department of Electrical Engineering, National Cheng Kung University, No. 1, Dasyue Road, Tainan City 701, Taiwan. appleway1984@gmail.com.

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Summary
This summary is machine-generated.

This study introduces a novel speech recognition system with advanced noise detection and reduction techniques. The system significantly enhances speech clarity in noisy environments, improving robot interaction and recognition accuracy.

Keywords:
automatic speech recognitionhuman-robot interactionnoise detectionnoise reduction

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Area of Science:

  • Speech Processing
  • Artificial Intelligence
  • Robotics

Background:

  • Interactive robots require robust speech recognition in noisy environments.
  • Ambient noise significantly degrades speech quality and recognition accuracy.

Purpose of the Study:

  • To develop an effective speech recognition system for noisy conditions.
  • To enhance speech quality and improve recognition rates for interactive robots.

Main Methods:

  • A two-stage system combining threshold-based noise detection and noise reduction.
  • Utilizing signal-to-noise ratio (SNR) for automatic speech enhancement.
  • Employing Independent Component Analysis (ICA) and Subspace Speech Enhancement (SSE) for noise reduction.

Main Results:

  • Achieved SNR improvement of 20 dB to 25 dB compared to noisy speech.
  • Enhanced speech recognition rates by 15% to 25%.
  • Demonstrated significant noise reduction in diverse noisy environments.

Conclusions:

  • The proposed system effectively mitigates noise interference in speech recognition.
  • Improved speech quality enhances the performance of interactive robotic systems.
  • The system offers a viable solution for reliable speech recognition in real-world noisy settings.